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A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the <jats:italic>Mycobacterium tuberculosis<\/jats:italic> genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (\u2018Hole finding protocol\u2019) coupled with the identification of candidate proteins for the predicted orphan enzyme (\u2018Hole filling protocol\u2019). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from <jats:italic>M. tuberculosis<\/jats:italic>, as a case study, which are most likely to perform the missing function.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The application of an automated homology-based enzyme identification protocol, ModEnzA, on <jats:italic>M. tuberculosis<\/jats:italic> genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using \u2018Hole finding protocol\u2019. The \u2018Hole-filling protocol\u2019 was validated on the <jats:italic>E. coli<\/jats:italic> genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>We have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as <jats:italic>M. tuberculosis<\/jats:italic>, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-020-03794-x","type":"journal-article","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T15:05:43Z","timestamp":1603119943000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study"],"prefix":"10.1186","volume":"21","author":[{"given":"Swati","family":"Sinha","sequence":"first","affiliation":[]},{"given":"Andrew M.","family":"Lynn","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1447-5360","authenticated-orcid":false,"given":"Dhwani K.","family":"Desai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"key":"3794_CR1","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","volume":"215","author":"SF Altschul","year":"1990","unstructured":"Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. 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